The present invention relates generally to a locomotive having a subsystem for cooling an engine therein, and, more particularly, to a system and method for predicting impending failures in the cooling subsystem.
As will be appreciated by those skilled in the art, a locomotive is a complex electromechanical system comprised of several complex subsystems. Each of these subsystems is built from components which over time fail The ability to systematically predict failures before they occur in the locomotive subsystems is desirable for several reasons. For example, in the case of the engine cooling subsystem, that ability is important for reducing the occurrence of primary failures which result in stoppage of cargo and passenger transportation. These failures can be very expensive in terms of lost revenue due to delayed cargo delivery, lost productivity of passengers, other trains delayed due to the failed one, and expensive on-site repair of the failed locomotive. Further, some of those primary failures could result in secondary failures that in turn damage other subsystems and/or components. It will be further appreciated that the ability to predict failures before they occur in the cooling subsystem would allow for conducting condition-based maintenance, that is, maintenance conveniently scheduled at the most appropriate time based on statistically and probabilistically meaningful information, as opposed to maintenance performed regardless of the actual condition of the subsystems, such as would be the case if the maintenance is routinely performed independently of whether the subsystem actually needs the maintenance or not.
Needless to say, a condition-based maintenance is believed to result in a more economically efficient operation and maintenance of the locomotive due to substantially large savings in cost. Further, such type of proactive and high-quality maintenance will create an immeasurable, but very real, good will generated due to increased customer satisfaction. For example, each customer is likely to experience improved transportation and maintenance operations that are even more efficiently and reliably conducted while keeping costs affordable since a condition-based maintenance of the locomotive will simultaneously result in lowering maintenance cost and improving locomotive reliability.
Previous attempts to overcome the above-mentioned issues have been generally limited to diagnostics after a problem has occurred, as opposed to prognostics, that is, predicting a failure prior to its occurrence. For example, previous attempts to diagnose problems occurring in a locomotive have been performed by experienced personnel who have in-depth individual training and experience in working with locomotives. Typically, these experienced individuals use available information that has been recorded in a log. Looking through the log, the experienced individuals use their accumulated experience and training in mapping incidents occurring in locomotive subsystems to problems that may be causing the incidents. If the incident-problem scenario is simple, then this approach works fairly well for diagnosing problems. However, if the incident-problem scenario is complex, then it is very difficult to diagnose and correct any failures associated with the incident and much less to prognosticate the problems before they occur.
Presently, some computer-based systems are being used to automatically diagnose problems in a locomotive in order to overcome some of the disadvantages associated with completely relying on experienced personnel. Once again, the emphasis on such computer-based systems is to diagnose problems upon their occurrence, as opposed to prognosticating the problems before they occur. Typically, such computer-based systems have utilized a mapping between the observed symptoms of the failures and the equipment problems using techniques such as a table look up, a symptom-problem matrix, and production rules. These techniques may work well for simplified systems having simple mappings between symptoms and problems. However, complex equipment and process diagnostics seldom have simple correspondences between the symptoms and the problems. Unfortunately, as suggested above, the usefulness of these techniques have been generally limited to diagnostics and thus even such computer-based systems have not been able to provide any effective solution to being able to predict failures before they occur.
In view of the above-mentioned considerations, there is a general need to be able to quickly and efficiently prognosticate any failures before such failures occur in the cooling subsystem of the locomotive, while minimizing the need for human interaction and optimizing the repair and maintenance needs of the subsystem so as to able to take corrective action before any actual failure occurs.
Generally speaking, the present invention fulfills the foregoing needs by providing a method for determining degradation in performance of an engine cooling subsystem having an electric motor for powering a fan in a locomotive. The method allows for monitoring a signal indicative of an electrical imbalance in at least one phase in the fan motor of the cooling subsystem, and for adjusting the value of the monitored signal for deviations from an estimated nominal phase signal value due to predetermined external variables so as to generate an adjusted phase signal value. The method further allows for comparing the value of the adjusted phase signal value against the nominal phase signal value to determine the performance of the cooling subsystem.
The present invention further fulfills the foregoing needs by providing a system for determining degradation in cooling subsystem performance in a locomotive having an engine cooled by a fan powered by a motor. The system is made up of a signal monitor coupled to monitor a signal indicative of an electrical imbalance in at least one phase in the fan motor of the cooling subsystem. A first module is coupled to the signal monitor to adjust the monitored signal for deviations from an estimated nominal phase signal value due to predetermined external variables to generate an adjusted phase signal value, and a second module is coupled to the first module to receive the adjusted phase signal value. The second module is configured to compare the value of the adjusted phase signal value against a nominal phase signal value to determine the performance of the cooling subsystem.